The problem is that theconvert.pycode for checking added tokens does not like it when it finds token IDs that are < thevocab_size. It will just error out and not try to make the FP16. So I have to handle this in
[0m File "/home/user1/.local/lib/python3.11/site-packages/torch/_utils_internal.py", line 87, in wrapper_function [36m(VLLM_Qwen_Chat_Ray pid=724236) [0m return function(*args, **kwargs) [36m(VLLM_Qwen_Chat_Ray pid=724236) [0m ^^^ [36m(VLLM_Qwen_Chat_Ray pid=724236) [...
import torch from torch.utils.data import Dataset class InstructionDataset(Dataset): def __init__(self, data, tokenizer): self.data = data # Pre-tokenize texts self.encoded_texts = [] for entry in data: instruction_plus_input = format_input(entry) response_text = f"\n\n### Response:...
import torch from torch.utils.data import Dataset class InstructionDataset(Dataset): def __init__(self, data, tokenizer): self.data = data # Pre-tokenize texts self.encoded_texts = [] for entry in data: instruction_plus_input = format_input(entry) response_text = f"\n\n### Response:...
Example #17Source File: bert_models.py From danlp with BSD 3-Clause "New" or "Revised" License 5 votes def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False): from transformers import BertTokenizer, BertForSequenceClassification # download the model or load the model path path_...
接下来,让我们实例化一个 example_dataset,然后用 PyTorch 的 DataLoader 创建一个 example_dataloader,以模拟我们稍后用于模型训练的data loader: import tiktoken from torch.utils.data import DataLoader tokenizer = tiktoken.get_encoding("gpt2") example_dataset = PreferenceDataset(example_data, tokenizer) exa...
in export return utils.export( File "/home/user/anaconda3/envs/swinocr/lib/python3.9/site-packages/torch/onnx/utils.py", line 163, in export _export( File "/home/user/anaconda3/envs/swinocr/lib/python3.9/site-packages/torch/onnx/utils.py", line 1074, in _export graph, params_dict...
File ~/transformers/src/transformers/tokenization_utils_fast.py:709, in PreTrainedTokenizerFast.train_new_from_iterator(self, text_iterator, vocab_size, length, new_special_tokens, special_tokens_map, **kwargs) [707](file:///home/jovyan/transformers/src/transformers/tokenization_utils_fast.py?li...
Next, we convert the text tokens into token IDs that we can process via embedding layers later From these tokens, we can now build avocabularythat consists of all the unique tokens all_words=sorted(set(preprocessed))vocab_size=len(all_words)print(vocab_size)1130vocab={token:integerforinteger...
importtorchfromtorch.utils.dataimportDatasetclassInstructionDataset(Dataset):def__init__(self,data,tokenizer):self.data=dataself.encoded_texts=[]forentryindata:instruction_plus_input=format_input(entry)response_text=f"\n\n### Response:\n{entry['output']}"full_text=instruction_plus_input+response...